Time series segmentation is a fundamental task in analyzing temporal data across various domains, from human activity recognition to energy monitoring. While numerous state-of-the-art methods have been developed to tackle this problem, the evaluation of their performance remains critically limited. Existing measures predominantly focus on change point accuracy or rely on point-based measures such as Adjusted Rand Index (ARI), which fail to capture the quality of the detected segments, ignore the nature of errors, and offer limited interpretability. In this paper, we address these shortcomings by introducing two novel evaluation measures: WARI (Weighted Adjusted Rand Index), that accounts for the position of segmentation errors, and SMS (State Matching Score), a fine-grained measure that identifies and scores four fundamental types of segmentation errors while allowing error-specific weighting. We empirically validate WARI and SMS on synthetic and real-world benchmarks, showing that they not only provide a more accurate assessment of segmentation quality but also uncover insights, such as error provenance and type, that are inaccessible with traditional measures.

翻译：时间序列分割是分析跨领域时序数据（从人类活动识别到能源监测）的一项基础任务。尽管已有大量先进方法被开发用于解决此问题，但其性能评估仍存在显著局限性。现有度量主要关注变点准确性或依赖基于点的度量（如调整兰德指数），这些方法无法捕捉检测片段的质量、忽略误差本质且可解释性有限。本文通过引入两种新颖的评估度量来解决这些缺陷：WARI（加权调整兰德指数）——考虑分割误差的位置信息，以及SMS（状态匹配分数）——一种细粒度度量，可识别并评分四种基本分割误差类型，同时支持误差特异性加权。我们在合成与真实基准数据集上对WARI和SMS进行实证验证，结果表明它们不仅能提供更准确的分割质量评估，还能揭示传统度量无法获取的洞察（如误差来源与类型）。